Implement attention with a KV cache for autoregressive generation.
Coding the KV cache shows you understand why decode is efficient: you append the new token's K/V and reuse the rest rather than recomputing. Interviewers look for the append-and-attend-over-cache logic. The implementation follows.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Coding the KV cache shows you understand why decode is efficient: you append the new token's K/V and reuse the rest rather than recomputing. Interviewers look for the append-and-attend-over-cache logic. The implementation follows.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
